Scalable cross-boundary edge framework

ABSTRACT

Apparatus and associated methods relate to automating building of a software solution for a specific intention that is cooperatively performed by a plurality of computing entities. A broadcasting computing entity broadcasts the specific intention, which includes announcing performance capability of a functional operation by the broadcasting computing entity over a network. One or more listening computing entities listening to such a broadcast then transmit a response to the broadcast. The response includes a subscription to an output of the functional operation. The broadcasting computing entity generates the output of the functional operation and provides the output generated to the one or more listening computing entities. In this way, the broadcasting computing entity advertises services that the broadcasting computing entity can provide, and the listening computing entities can avail themselves of services needed, all in an automated fashion.

BACKGROUND

Automated systems are used in a wide variety of applications, such as, for example, controlling industrial processes, monitoring people, surveilling property, evaluating health conditions of people or equipment, providing automated control of homes or devices, etc. Such automated systems can include various interconnected modules and/or hardware components, such as, for example: sensors, transducers, cameras, and other various hardware components. Coordination and control of such interconnected modules and/or hardware components is performed using one or more computing entities. Cloud-based computing entities, local computing entities, or some combination thereof can be programmed to provide such coordination and control of these interconnected modules and/or hardware components of automated systems.

In some embodiments, an automated system or subsystem can be arranged as local devices centrally connected via at least one hub (e.g., an IoT hub). Edge computing approaches to such automated systems can centralize local collections (e.g., subsystems) of interconnected modules and/or hardware devices around edge hubs that serve as a local nexus for distributed computation and information storage closer to those subsystem modules, and as a gateway to cloud-based (i.e., remote) services provided through the hub. Such local subsystems can operate independently or semi-independently from their parent hub, when isolated.

In many applications, edge systems (e.g., IoT edge systems) can simultaneously provide many of the benefits of cloud-based computing solutions while retaining the capacity for independent local operation and reducing inter-network bandwidth requirements. By contrast, substantially fully cloud-based systems use local devices that perform minimal local processing, and offload more complex processing tasks and tasks involving data aggregated from multiple devices to a nonlocal cloud service. Such approaches can become burdensome as the volume of data involved increases. Edge systems instead preferentially perform most routine processing tasks within the local subsystem, with an edge hub coordinating communications between local devices and handling many processing tasks, including processing of data aggregated from multiple devices within the local subsystem. The edge hub can communicate with cloud systems for remote subsystem access, remote and/or distributed data persistence, high level (i.e., multi-subsystem) control and analytics, and other purposes.

Development frameworks facilitate developers to generate code that coordinates and controls the interconnected modules and/or hardware components of automated systems.

SUMMARY

Apparatus and associated methods relate to a system to automate building a software solution that is cooperatively performed amongst a plurality of computing entities. The system includes a broadcasting computing entity configured to connect to a network and computer readable memory. The computer readable memory contains instructions that when executed by the broadcasting computing entity causes the system to: i) connect to the network; ii) broadcast, via the network, a specific intention that includes announcing performance capability of a functional operation by the broadcasting computing entity; iii) receive a response to the specific intention from a listening computing entity via the network, the response including a subscription to an output of the functional operation; iv) generate the output of the functional operation in response to receiving the subscription from the listening computing entity; and v) provide the output of the functional operation to the listening computing entity, thereby performing a part of the software solution in cooperation with the listening computing entity.

Some embodiments relation to a method to automate building a software solution that is cooperatively performed amongst a plurality of computing entities. The method includes connecting, by a broadcasting computing entity, to a network. The method includes broadcasting, by the broadcasting computing entity via the network, a specific intention that includes announcing performance capability of a functional operation by the broadcasting computing entity. The method includes receiving, by the broadcasting computing entity, a response to the specific intention from a listening computing entity via the network. The response includes a subscription to an output of the functional operation. The method includes generating, by the broadcasting computing entity, the output of the functional operation in response to receiving the subscription from the listening computing entity. The method also includes providing, by the broadcasting computing entity, the output of the functional operation to the listening computing entity, thereby performing a part of the software solution in cooperation with the listening computing entity.

Some embodiments relate to a system to automate building of a software solution that is cooperatively performed by a plurality of computing entities. the system includes computer readable memory and a listening computing entity configured to be connected to a network. The computer readable memory contains instructions that when executed by the listening computing entity causes the system to: i) connect to the network; ii) listen, via the network, for a specific intention broadcast by a broadcasting computing entity. The specific intention includes announcing performance capability of a functional operation by the broadcasting computing entity; iii) receive the specific intention broadcast by the broadcasting computing entity; iv) compare the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs; v) subscribe, via the network, to an output of the functional operation performed by the broadcasting computing entity in response to the functional operation being listed in the list of operational needs; vi) receive the output of the functional operation performed by the broadcasting computing entity; and vii) perform an action using the output of the functional operation received from the broadcasting computing entity, thereby performing a part of the software solution in cooperation with the broadcasting computing entity.

Some embodiments relate to a method to automate building of a software solution that is cooperatively performed by a plurality of computing entities. The method includes connecting, by a listening computing entity, to a network. The method includes listening, by the listening computing entity via the network, for a specific intention broadcast by a broadcasting computing entity. The specific intention includes announcing performance capability of a functional operation by the broadcasting computing entity. The method includes receiving, by the listening computing entity, the specific intention broadcast by the broadcasting computing entity. The method includes comparing, by the listening computing entity, the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs. The method includes subscribing, by the listening computing entity via the network, to an output of the functional operation performed by the broadcasting computing entity in response to the functional operation being listed in the list of operational needs. The method includes receiving, by the listening computing entity, the output of the functional operation performed by the broadcasting computing entity. The method also includes performing, by the listening computing entity, an action using the output of the functional operation received from the broadcasting computing entity, thereby performing a part of the software solution in cooperation with the broadcasting computing entity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system for to automating building a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities.

FIG. 2 is a block diagram of a computing entity that coordinates performance of a task with another computing entity.

FIG. 3 is a flow chart of an embodiment of a method for providing a functional operation for other computing entities.

FIG. 4 is a flow chart of an embodiment of a method for subscribing to a functional operation for other computing entities.

FIG. 5 is a schematic diagram of an imaging system in which a software solution is built in an automated fashion.

DETAILED DESCRIPTION

Apparatus and associated methods relate to automating building of a software solution for a specific intention that is cooperatively performed by a plurality of computing entities. A broadcasting computing entity broadcasts the specific intention, which includes announcing performance capability of a functional operation by the broadcasting computing entity over a network. One or more listening computing entities listening to such a broadcast then transmit a response to the broadcast. The response includes a subscription to an output of the functional operation. The broadcasting computing entity generates the output of the functional operation and provides the output generated to the one or more listening computing entities. In this way, the broadcasting computing entity advertises services that the broadcasting computing entity can provide, and the listening computing entities can avail themselves of services needed, all in an automated fashion.

When a collection of automated components (e.g., sensors, transducers, cameras, controllers, gateways, etc.) is deployed to perform various tasks corresponding to a specific application (i.e., a specific use case), such collection of automated components communicate with one another so as to cooperatively perform the various tasks corresponding to the application. Each of such tasks can often cooperatively be performed by multiple threads of a single automated component (i.e., scaling up of the task) and/or across multiple automated components of the collection (i.e., scaling out of the task). Such scaling up and scaling out of such tasks can be automated freeing a developer from having to write custom code for such scaling processes. Furthermore, some tasks can involve some level of cooperation amongst multiple automated components. For example, surveilling property can involve capturing imagery via a camera, and then using a computing entity to perform image analysis, so as to determine if a person is discernable within the captured imagery. Such cooperation amongst multiple automated components can also be automated, again freeing the developer from having to write custom code for such cooperative tasks. Herein, such activities, as described immediately above, are called automated building of a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities.

FIG. 1 is a schematic diagram of a system for automating building a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities. In FIG. 1 , automated system 10 includes various automated components—local computing entities 12 a-12 c and local modules 14 a-14 d. Local modules 14 a-14 d can be devices that interact with the physical world, such as, for example, sensors, actuators, transducers, cameras, etc. Typically, local modules 14 a-14 d are devices that convert between a signal and a physical parameter. Computing entities 12 a-12 c can be state machines or processors that perform execute instructions contained in computer readable memory (e.g., computer code), such as, for example, controllers, computers, gateways, etc. In the FIG. 1 depiction, local computing entity 12 a includes local module 14 a. Such a device can be, for example, a smart sensor that has computing capabilities. Automated system 10 has been deployed to perform one or more tasks corresponding to a specific use case (i.e., a specific application). Use cases are essentially applications for which the local modules 14 a-14 d and local computing entities 12 a-12 c have been deployed. Various use cases correspond to various applications for which local modules 14 a-14 d and local computing entities 12 a-12 c can be configured to address. For example, use cases can include monitor a cold-temperature storage facility, operating a chemical processing facility, controlling a final product assembly line, providing security monitoring of a facility, monitoring health of a population, or myriad other applications that have tasks, which can be automated using local modules and local computing entities, such as those depicted in FIG. 1 .

Local modules, such as local modules 14 a-14 d, can be any device that facilitates such tasks corresponding to the use case for which local modules 14 a-14 d are deployed. For example, for a use case of monitoring a cold-storage facility, local modules 14 a-14 d might include one or more temperature sensor. For a use case of surveilling a property, local modules 14 a-14 d might include a camera, a thermal-imaging camera, or a motion detector, for example. For a use case of controlling a chemical processing facility, local modules 14 a-14 d might include flow controllers, heaters, valve controllers, pressure sensors, pH sensors, etc. For a use case of controlling a final product assembly line, local modules 14 a-14 d might include robotic arms, cameras, conveyor belts motors, etc. For these and many other use cases, many tasks that are cooperatively performed amongst a plurality of computing entities can be built automatically as will be described in detail below.

In addition to local communications amongst local modules 14 a-14 d and local computing entities 12 a-12 c via local network 16, automated system 10 can communicate via cloud 18 with cloud-based computing entities 20 a-20 e, which can be configured to perform various back-end services. Such back-end services can include, for example, configuration services, organization services, notification services, location/device services, alert services, telemetry services, device-cloud interface, synchronization services, database services (e.g., Cosmos database, SQL database, etc.), IoT hub services, front-end User Interface (UI) services, health monitoring services, etc. Automated building of a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities can include tasks cooperatively performed amongst combinations of local computing entities 12 a-12 c and cloud-based computing entities 20 a-20 e (e.g., combinations of two or more local computing entities 12 a-12 c only, combinations of two or more cloud-based computing entities 20 a-20 e only, or combinations of one or more local computing entities 12 a-12 c and one or more cloud-based computing entities 20 a-20 e).

In FIG. 1 , local computing entities 12 a-12 c and cloud-based computing entities 20 a-20 e variously execute software so as to perform various tasks or functions of or related to automated system 10. The various tasks or functions of or related to automated system 10 can require various levels of coordination between two or more of local computing entities 12 a-12 c and cloud-based computing entities 20 a-20 e. For example, some back-end services might need little coordination between local computing entities 12 a-12 c and cloud-based computing entities 20 a-20 e. Higher levels of coordination can provide beneficial performance for other tasks or functions of or related to automated system 10, especially for functions and/or operations locally performed by local computing entities 12 a-12 c using local modules 14 a-14 d. Thus, although tasks or functions can be cooperatively performed by local computing entities and cloud-based computing entities, below will be described only tasks or functions cooperatively performed by local computing entities 12 a-12 c.

To facilitate such coordination of functions and/or operations locally performed by local computing entities 12 a-12 c and local modules 14 a-14 d, local computing entities 12 a-12 c have been programmed so to facilitate such coordination of functions and/or operations. Such coordination of functions and/or operations can be performed in various manners. In one embodiment, for example, local computing entity 12 a connects to local network 16, to which local computing entities 12 b and 12 c are also connected. Local computing entity 12 a broadcasts a specific intention via local network 16. The specific intention includes an announcement of a performance capability of a functional operation that local computing entity 12 a can provide. Local computing entity 12 a can be considered a broadcasting computing entity by virtue of its broadcast of its specific intention. The specific intention broadcast by broadcasting computing entity 12 a can be any capability of which it is capable of doing. For example, the broadcasting computing entity can broadcast: i) any capabilities for which it has been programmed to perform; and/or ii) any capabilities associated with local module 14 a with which it interfaces. Local computing entities 12 b and 12 c listen to the broadcast of local computing entity 12 a. For example, if local module 14 a is a sensor, broadcasting computing entity can broadcast a capability of providing data indicative of a metric sensed by local module 14 a. Local broadcasting module might be programmed to provide various data processing functions upon such data indicative of a sensed parameter, such as, for example, filtering, histogramming, etc.

Local computing entity 12 b perhaps has been programmed to listen for specific intentions broadcast from broadcasting computing entities. Local computing entity 12 b can be considered a listening computing entity by virtue of its listening for specific intentions broadcast by broadcasting computing entities. In some embodiments, listening computing entity 12 b can compare the specific intentions broadcast with one or more specific needs listed in a list of operational needs. In other embodiments, listening computing entity 12 b assess whether listening computing entity 12 b can benefit from the specific intentions broadcast in different manners. In either case, listening computing entity 12 b transmits a communication in response to determining that listening computing entity 12 b could use the specific intention broadcast by broadcasting computing entity 12 a.

Broadcasting computing entity 12 a receives the response to the specific intention from a listening computing entity 12 b via local network 16. The response can include a subscription to an output of the functional operation, for example. Broadcasting computing entity 12 a performs the functional operation declared in the specific intention broadcast and generates an output of the functional operation. Broadcasting computing entity 12 b provides the output generated to the listening computing entity. In some embodiments such provision of the output to subscribing computing entities is performed using publication/subscription engines.

The above-described system and/or method of automating building a software solution can be used using network configurations that are different from that depicted in FIG. 1 . For example, local modules 14 a-14 d can be directly connect to local network 16, directly connect to one or more of computing entities 12 a-12 c, and/or connect via cloud 18 to local computing entities 12 a-12 c and cloud-based computing entities 20 a-20 e. Such connections can be wired or wireless. Such connections can use any available networking protocols.

FIG. 2 is a block diagram of a computing entity that coordinates performance of a task with another computing entity. In FIG. 2 , computing entity 12 includes network interface 22, processor 24, computer readable memory 26 containing instructions 28 that, when executed by processor 24 cause computing entity 12 to perform various steps for automating building a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities. Examples of computing entity 12 can include a smart sensor or actuator, an edge computer, a gateway computer, etc. Computing entity 12, as disclosed in the FIG. 2 embodiment, can be used for both broadcasting a specific intention as well as listening to broadcasts from other computing entities. Local computing entity 12 can be any of those local computing entities depicted in FIG. 1 , for example (i.e., local computing entities 12 a-12 c).

To perform the various steps for automating building a software solution for a specific intention that is cooperatively performed amongst a plurality of computing entities, processor 24 can read program instructions 28 from computer readable memory 26, which cause computing entity 12 to: i) connect to a network; ii) broadcast the specific intention which includes announcing performance capability of a functional operation by the broadcasting computing entity; iii) receive a response to the specific intention from a listening computing entity via the network, each response including a subscription to an output of the functional operation; iv) generate the output of the functional operation; and v) provide the output generated to the listening computing entity. When performing the above operations, computing entity 12 is operating as a broadcasting computing entity. Examples of processor 24 can include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other equivalent discrete or integrated logic circuitry.

Computing entity 12 can also operate as a listening computing entity. When operating as a listening computing entity, processor 24 can read program instructions 28 from computer readable memory 26, which cause computing entity 12 to: i) connect to a network; ii) listen for a specific intention broadcast by a broadcasting computing entity; ii) compare the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs; iv) subscribe to an output of the functional operation performed by the broadcasting computing entity in response to the functional operation being listed in the list of operational needs; and v) receive the output of the functional operation performed by the broadcasting computing entity).

Computer-readable memory 26 can be configured to store information obtained and/or computed during operation of computing entity 12. Computer-readable memory 26, in some examples, is described as computer-readable storage media. In some examples, a computer-readable storage medium can include a non-transitory medium. The term “non-transitory” can indicate that the storage medium is not embodied in a carrier wave or a propagated signal. In certain examples, a non-transitory storage medium can store data that can change over time (e.g., in RAM or cache). In some examples, computer-readable memory 26 is a temporary memory, meaning that a primary purpose of computer-readable memory 26 is not long-term storage. Computer-readable memory 26, in some examples, is described as volatile memory, meaning that computer-readable memory 26 do not maintain stored contents when power to IoT device interface system 36 is turned off. Examples of volatile memories can include random-access memories (RAM), dynamic random-access memories (DRAM), static random-access memories (SRAM), and other forms of volatile memories. In some examples, computer-readable memory 26 is used to store program instructions for execution by processor 24. Computer-readable memory 26, in one example, is used by software or applications running on computing entity 12 (e.g., a software program performing such scaling up or scaling out of tasks) to temporarily store information during program execution.

In some examples, computer-readable memory 26 can also include one or more computer-readable storage media. Computer-readable memory 26 can be configured to store larger amounts of information than volatile memory. Computer-readable memory 26 can further be configured for long-term storage of information. In some examples, computer-readable memory 26 includes non-volatile storage elements. Examples of such non-volatile storage elements can include magnetic hard discs, optical discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.

Network interface 22, in one example, is configured to facilitate communication between computing entity 12 and external devices via one or more networks, such as one or more wireless or wired networks or both. Network interface 22 can include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. Other examples of such network interfaces can include Bluetooth, 3G, 4G, and Wi-Fi radio computing devices as well as Universal Serial Bus (USB).

FIG. 3 is a flow chart of an embodiment of a method for providing a functional operation for other computing entities. In FIG. 3 , method 30 is disclosed from the perspective of a broadcasting computing entity, such as broadcasting computing entity 12 a depicted in FIG. 1 . Method 30 begins at step 32, where broadcasting computing entity connects to local network 16. Then, at step 34, broadcasting computing entity 12 a broadcasts, via local network 16, the specific intention. The specific intention broadcast includes announcing performance capability of a functional operation by broadcasting computing entity 12 a. In some embodiments, the specific intention can further include instructions for a listening entity to communicate with broadcasting computing entity 12 a. In some embodiments, broadcasting computing entity 12 a encrypts the specific intention using an encryption algorithm known to both broadcasting computing entity 12 a and listening computing entities.

At step 36, broadcasting computing entity 12 a waits to receive a response to the specific intention from a listening computing entity via the network. A response will include a subscription to an output of the functional operation. If a response is received, method 30 advances to step 38, where broadcasting computing entity 12 a generates the output of the functional operation. At step 40, broadcasting computing entity 12 a provides the output generated to the listening computing entity. After providing the output at step 40, method 30 returns to step 34 and broadcasts the specific intention again. Such periodic broadcasting of its intention permits listening computing entities that are later connected to the network to find broadcasting computing entities that provide operations that are on their list of operational needs. Such periodic broadcasting also permits subscribing computing devices with periodic indicators that the broadcasting computing entity to which they subscribe is still operating and online. Should these periodic broadcasts cease to be heard, such listening devices can search for other providers or assume responsibility for performing such operations themselves.

In some embodiments, the output of the functional operation is/are based on input data. Such input data can be provided by a listening computing entity or a local module, for example. In embodiments in which input data is provided by a listening computing entity, broadcasting computing entity 12 a receives such input data from the listening computing entity via the network. Then, broadcasting computing entity 12 a computes the output based on the input data received from the listening computing entity. Different listening computing entity can provide different input data to broadcasting computing entity 12 a so as to have broadcasting computing entity 12 a compute different outputs for each of these different sets of input data. In such an embodiment, broadcasting computing entity 12 a provides each input computing entity with an output corresponding to the input data provided thereby.

Using the methods of method 30, broadcasting computing entity 12 a performs a part of the software solution in cooperation with the listening computing entity. The part performed by broadcasting computing entity 12 a includes, at least, performance of the functional operation. Listening computing entity then uses the output of the functional operation to perform an action that is part of the software solution.

FIG. 4 is a flow chart of an embodiment of a method for subscribing to a functional operation for other computing entities. In FIG. 4 , method 50 is disclosed from the perspective of a listening computing entity, such as listening computing entity 12 b depicted in FIG. 1 . Method 50 begins at step 52, where listening computing entity 12 b connects to a network. At step 54 listening computing entity 12 b listens, via the network, for a specific intention broadcast by a broadcasting computing entity. The specific intention will include an announcement of a performance capability of a functional operation by the broadcasting computing entity. In some embodiments, the specific intention can further include instructions for listening entity computing 12 b to communicate with the broadcasting computing entity. In some embodiments, the specific intention has been encrypted by the broadcasting computing entity. In such embodiments, listening computing entity 12 b decrypts the specific intention using an encryption algorithm used to encrypt the specific intention by the broadcasting computing entity.

In some examples, listening computing entity 12 b listens for a specific intention broadcast by a broadcasting computing entity in response to determining that a number of currently-active subscriptions associated with listening computing entity 12 b is less than (or equal to) a threshold number of currently-active subscriptions, such as one subscription, two subscriptions, or more currently-active subscriptions. For instance, listening computing entity 12 b can determine (e.g., based on a stored threshold number) whether a maximum threshold number of currently-active subscriptions to which listening computing entity 12 b is to simultaneously subscribe is currently met. In response to determining that the number of currently-active subscriptions to which listening computing entity 12 b is currently subscribed is less than the threshold number of subscriptions, listening computing entity 12 b can listen for the specific intention broadcast by a broadcasting computing entity. In response to determining that the number of currently-active subscriptions to which listening computing entity 12 b is currently subscribed is equal to (or exceeds) the threshold number of subscriptions, listening computing entity 12 b can refrain from listening for a broadcast specific intention until the number of currently-active subscriptions to which listening computing entity 12 b is currently subscribed is less than the threshold number of subscriptions (e.g., after listening computing entity 12 b unsubscribes or otherwise terminates a number of subscriptions sufficient to bring the number of currently-active subscriptions below the threshold number).

If, at step 54, listening computing entity 12 b hears (i.e., receives) a specific intention that meets a criterion of listening computing entity 12 b, then method 30 continues to step 56, where listening computing entity 12 b compares the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs. If the functional operation is listed in the list of operational needs, then method 50 continues to step 58, where listening computing entity 12 b subscribes to an output of the functional operation performed by the broadcasting computing entity.

At step 60 listening computing entity 12 b receives the output of the functional operation performed by the broadcasting computing entity. In some embodiments, the output of the functional operation is based on input data. Such input data can be provided by listening computing entity 12 b or a local module, for example. In embodiments in which input data is provided by listening computing entity 12 b, listening computing entity 12 b transmits such input data to the broadcasting computing entity via the network. Then, after the broadcasting computing entity computes the output based on the input data transmitted to the broadcasting computing entity, such output will be provided to listening computing entity 12 b.

Listening computing entity 12 b then performs some action using the output of the functional operation received from broadcasting computing entity 12 a. This action performed using the output of the functional operation is a part of the software solution of the system. Such an action is performed by listening computing entity 12 b includes. This cooperation of broadcasting computing entity 12 a and listening computing entity 12 b is automatically performed using methods 30 and 50, without human intervention.

Deployment of an automated system can involve one or more computing entities, such as, for example, local computing entities 12 a-12 c (depicted in FIG. 1 ) as well as one or more modules, such as, for example, local modules 14 a-14 d (depicted in FIG. 1 ). Each of local computing entities 12 a-12 c and each of local modules 14 a-14 d are deployed for a reason—to perform some particular task or tasks. When a system involves multiple components, such as those depicted in FIG. 1 , such components often need to communicate with one another and/or coordinate performance of their respective tasks. In the past, such communication and coordination has been accomplished by a developer writing custom software for the particular automated system requiring the specific combination of local computing entities 12 a-12 c and each of local modules 14 a-14 d. Using the above-described methods 30 and 50, such communication and coordination can be automated, without need for custom software development. Broadcasting of specific intentions (i.e., functional operations), of which a local computing module is capable to perform, makes known to listening computing entities that such operational functions are offered by the broadcasting computing entity, should it be helpful (e.g., listed on an operations list) to any listening computing entities. When a listening entity avails itself of an offered operational function, that listening computing entity offloads a portion of the tasks it needed to perform to another computing entity—the broadcasting computing entity. In this way, the computing effort (e.g., the tasks) of the entire automated system can distributed amongst the various components. Such a distribution can result in: i) a leveling of the computing effort performed by the various components; ii) the automatic finding of a broadcasting computing entity capable of performing a specific functional operation that a listening computing entity is incapable of or inefficient in performing itself; iii) an optimization in which tasks are performed by computing entities most capable of such performance (e.g., equipped with specific hardware to perform such tasks); and/or iv) elimination of redundant performance of tasks, should two or more listening computing entities response to a specific intention broadcasted.

FIG. 5 is a schematic diagram of an imaging system in which a software solution is built in an automated fashion. In FIG. 5 imaging system 110 includes edge computer 112, cameras 114 a-114 c, user computer 70 and display screen 72. Edge computer 112 operates as a local computing entity, such as local computing entities 12 a-12 c, as depicted in FIG. 1 . Cameras 114 a-114 c can operate as local modules, similar to local modules 14 a-14 d, as depicted in FIG. 1 . Imaging system 110 has been deployed to monitor a crowd at a public facility. Some of the functional outputs of imaging system 110 include: i) a count of the number of persons in the public facility; and ii) determination of density of persons as a function of location. Imaging cameras 114 a-114 c are positioned in such a manner that the entire facility is imaged by at least one of imaging cameras 114 a-114 c.

Edge computer 112, when connected to the network, broadcasts its specific intention, which includes various functional operations, such as, for example, image correlation, person identification, counting algorithms, density algorithms, etc. User computer 70 is running an application that provides the functional outputs of: i) displaying the raw image data from any cameras imaging the public facility; ii) counting the numbers of persons at the public facility and provides an index of such a count; and iii) calculating the local densities of persons and annotating the displayed imagery to indicate areas of low and high densities. After listening to such a broadcast by edge computer 112 and receiving the specific intention broadcast, user computer 70 subscribes to the functional operations broadcast thereby.

Each of cameras 114 a-114 c, when connected to the network, broadcasts its specific intention, which includes providing streaming imagery of the scene aligned within its field of view. Cameras 114 a-114 c function as both a broadcasting computing entity and a local module in this embodiment. The broadcasting of the specific intentions is described above as an action of a computing entity, while the imaging of data is described above as an action of an image sensing module. Such combined functions are depicted in FIG. 1 with reference to local computing entity 12 a and local module 14 a.

Edge computer 112 subscribes to the streaming imagery broadcast by each of cameras 114 a-114 c, which will be used as inputs for the functional operations provided by edge computer 112. For example, edge computer 112 will correlate concurrent frames provided by cameras 114 a-114 c, so as to avoid double counting of persons who are simultaneously imaged by two or more of cameras 114 a-114 c. Edge computer 112 will then count persons and calculate the local densities of persons, and then provide functional outputs of such functional operations, which will be disseminated to any and all computing entities subscribing to such functional operations, such as user computer 70.

User computer 70 also subscribes to the streaming imagery broadcast by each of cameras 114 a-114 c, which will be displayed on display screen 70. User computer will display an index of the count of persons on the display screen as well as annotating the displayed imagery with indicia of local densities of person. All of the above-described coordination of tasks is performed automatically by the various computing entities of imaging system 110. No custom computer code is required for these separate entities—edge computer 112, cameras 114 a-114 c and user computer 70 to coordinate amongst themselves so as to provide functional outputs and obtain functional outputs needed for successful accomplishment of their tasks.

While the invention has been described with reference to an exemplary embodiment, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims. 

1. A method to automate building a software solution that is cooperatively performed amongst a plurality of computing entities, the method comprising: connecting, by a broadcasting computing entity, to a network; broadcasting, by the broadcasting computing entity via the network, a specific intention that includes announcing performance capability of a functional operation by the broadcasting computing entity; receiving, by the broadcasting computing entity, a response to the specific intention from a listening computing entity via the network, the response including a subscription to an output of the functional operation; generating, by the broadcasting computing entity, the output of the functional operation in response to receiving the subscription from the listening computing entity; and providing, by the broadcasting computing entity, the output of the functional operation to the listening computing entity, thereby performing a part of the software solution in cooperation with the listening computing entity.
 2. The method of claim 1, wherein the specific intention further includes instructions for communicating with the broadcasting computing entity.
 3. The method of claim 1, further including: encrypting, by the broadcasting computing entity, the specific intention using an encryption algorithm known to the listening computing entity.
 4. The method of claim 1, wherein the output of the functional operation is based on input data.
 5. The method of claim 4, further comprising: receiving, by the broadcasting computing entity, input data from the listening computing entity via the network.
 6. The method of claim 5, wherein the output is generated based on the input data received from the first listening computing entity.
 7. The method of claim 1, wherein the listening computing entity is a first listening computing entity and the response is a first response, the method further comprising: receiving, by the broadcasting computing entity, a second response to the specific intention from a second listening computing entity via the network, the response including a subscription to an output of the functional operation; providing, by the broadcasting computing entity, the output of the functional operation generated to the second listening computing entity, thereby performing a part of the software solution in cooperation with the second listening computing entity.
 8. A method to automate building of a software solution that is cooperatively performed by a plurality of computing entities, the method comprising: connecting, by a listening computing entity, to a network; listening, by the listening computing entity via the network, for a specific intention broadcast by a broadcasting computing entity, the specific intention including announcing performance capability of a functional operation by the broadcasting computing entity; receiving, by the listening computing entity, the specific intention broadcast by the broadcasting computing entity; comparing, by the listening computing entity, the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs; subscribing, by the listening computing entity via the network, to an output of the functional operation performed by the broadcasting computing entity in response to the functional operation being listed in the list of operational needs; receiving, by the listening computing entity, the output of the functional operation performed by the broadcasting computing entity; and performing, by the listening computing entity, an action using the output of the functional operation received from the broadcasting computing entity, thereby performing a part of the software solution in cooperation with the broadcasting computing entity.
 9. The method of claim 8, wherein the specific intention further includes instructions for communicating with the broadcasting computing entity, wherein subscribing to the output of the function operation comprises: transmitting, by the listening computing entity, a response to the specific intention received from the broadcasting computing using the instructions for communicating with the broadcasting computing entity.
 10. The method of claim 8, further including: decrypting, by the listening computing entity, the specific intention using an encryption algorithm used to encrypt the specific intention by the broadcasting computing entity.
 11. The method of claim 8, wherein the output of the functional operation is based on input data.
 12. The method of claim 11, further comprising: transmitting, by the listening computing entity, input data to the broadcasting computing entities via the network.
 13. A system to automate building a software solution that is cooperatively performed amongst a plurality of computing entities, the system comprising: a broadcasting computing entity configured to connect to a network; computer readable memory containing instructions that when executed by the broadcasting computing entity causes the system to: connect to the network; broadcast, via the network, a specific intention that includes announcing performance capability of a functional operation by the broadcasting computing entity; receive a response to the specific intention from a listening computing entity via the network, the response including a subscription to an output of the functional operation; generate the output of the functional operation in response to receiving the subscription from the listening computing entity; and provide the output of the functional operation to the listening computing entity, thereby performing a part of the software solution in cooperation with the listening computing entity.
 14. The system of claim 13, wherein the specific intention further includes instructions for communicating with the broadcasting computing entity.
 15. The system of claim 13, wherein the computer readable memory contains further instructions that when executed by the broadcasting computing entity causes the system to: encrypt the specific intention using an encryption algorithm known to the listening computing entity.
 16. The system of claim 13, wherein the output of the functional operation is based on input data.
 17. The system of claim 16, wherein the computer readable memory contains further instructions that when executed by the broadcasting computing entity causes the system to: receive input data from the listening computing entity via the network.
 18. The system of claim 17, wherein the listening computing entity is a first listening computing entity and the response is a first response, the computer readable memory contains further instructions that when executed by the broadcasting computing entity causes the system to: receive a response to the specific intention from a second listening computing entity via the network, the response including a subscription to an output of the functional operation; provide the output of the functional operation generated to the second listening computing entity, thereby performing a part of the software solution in cooperation with the second listening computing entity.
 19. The system of claim 18, wherein providing an output to the listening computing entity includes: providing the first output to the first listening computing entity; and providing the second output to the second listening computing entity.
 20. A system to automate building of a software solution that is cooperatively performed by a plurality of computing entities, the system, comprising: a listening computing entity configured to be connected to a network; computer readable memory containing instructions that when executed by the listening computing entity causes the system to: connect to the network; listen, via the network, for a specific intention broadcast by a broadcasting computing entity, the specific intention including announcing performance capability of a functional operation by the broadcasting computing entity; receive the specific intention broadcast by the broadcasting computing entity; compare the functional operation included in the specific intention broadcast by the broadcasting computing entity with a list of operational needs; subscribe, via the network, to an output of the functional operation performed by the broadcasting computing entity in response to the functional operation being listed in the list of operational needs; receive the output of the functional operation performed by the broadcasting computing entity; and perform an action using the output of the functional operation received from the broadcasting computing entity, thereby performing a part of the software solution in cooperation with the broadcasting computing entity.
 21. The system of claim 20, wherein the specific intention further includes instructions for communicating with the broadcasting computing entity, wherein subscribing to the output of the function operation comprises: transmitting a response to the specific intention received from the broadcasting computing using the instructions for communicating with the broadcasting computing entity.
 22. The system of claim 20, wherein the computer readable memory contains further instructions that when executed by the listening computing entity causes the system to: decrypt the specific intention using an encryption algorithm used to encrypt the specific intention by the broadcasting computing entity.
 23. The system of claim 30, wherein the output of the functional operation is/are based on input data.
 24. The system of claim 23, wherein the computer readable memory contains further instructions that when executed by the listening computing entity causes the system to: transmit input data to the broadcasting computing entity via the network. 